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2020 – today
- 2024
[j6]Yuchang Zhu, Jintang Li, Liang Chen, Zibin Zheng:
FairAGG: Toward Fair Graph Neural Networks via Fair Aggregation. IEEE Trans. Comput. Soc. Syst. 11(5): 6308-6319 (2024)- [c18]Jintang Li, Huizhe Zhang, Ruofan Wu, Zulun Zhu, Baokun Wang, Changhua Meng, Zibin Zheng, Liang Chen:
A Graph is Worth 1-bit Spikes: When Graph Contrastive Learning Meets Spiking Neural Networks. ICLR 2024 - [c17]Yunfei Liu
, Jintang Li, Yuehe Chen, Ruofan Wu, Ericbk Wang, Jing Zhou, Sheng Tian, Shuheng Shen, Xing Fu, Changhua Meng, Weiqiang Wang, Liang Chen:
Revisiting Modularity Maximization for Graph Clustering: A Contrastive Learning Perspective. KDD 2024: 1968-1979 - [c16]Yuchang Zhu, Jintang Li, Yatao Bian, Zibin Zheng, Liang Chen:
One Fits All: Learning Fair Graph Neural Networks for Various Sensitive Attributes. KDD 2024: 4688-4699 - [c15]Zulun Zhu, Kai Wang, Haoyu Liu, Jintang Li, Siqiang Luo:
Topology-monitorable Contrastive Learning on Dynamic Graphs. KDD 2024: 4700-4711 - [c14]Wangbin Sun, Jintang Li, Liang Chen, Bingzhe Wu, Yatao Bian, Zibin Zheng:
Rethinking and Simplifying Bootstrapped Graph Latents. WSDM 2024: 665-673 - [c13]Yuchang Zhu, Jintang Li, Liang Chen, Zibin Zheng:
The Devil is in the Data: Learning Fair Graph Neural Networks via Partial Knowledge Distillation. WSDM 2024: 1012-1021 - [c12]Yuchang Zhu, Jintang Li, Zibin Zheng, Liang Chen:
Fair Graph Representation Learning via Sensitive Attribute Disentanglement. WWW 2024: 1182-1192 - [i28]Huizhe Zhang, Jintang Li, Liang Chen, Zibin Zheng:
SGHormer: An Energy-Saving Graph Transformer Driven by Spikes. CoRR abs/2403.17656 (2024) - [i27]Yuchang Zhu, Jintang Li, Zibin Zheng, Liang Chen:
Fair Graph Representation Learning via Sensitive Attribute Disentanglement. CoRR abs/2405.07011 (2024) - [i26]Jintang Li, Ruofan Wu, Xinzhou Jin, Boqun Ma, Liang Chen, Zibin Zheng:
State Space Models on Temporal Graphs: A First-Principles Study. CoRR abs/2406.00943 (2024) - [i25]Yuchang Zhu, Jintang Li, Yatao Bian, Zibin Zheng, Liang Chen:
One Fits All: Learning Fair Graph Neural Networks for Various Sensitive Attributes. CoRR abs/2406.13544 (2024) - [i24]Yunfei Liu, Jintang Li, Yuehe Chen, Ruofan Wu, Ericbk Wang, Jing Zhou, Sheng Tian, Shuheng Shen, Xing Fu, Changhua Meng, Weiqiang Wang, Liang Chen:
Revisiting Modularity Maximization for Graph Clustering: A Contrastive Learning Perspective. CoRR abs/2406.14288 (2024) - [i23]Xinzhou Jin, Jintang Li, Liang Chen, Chenyun Yu, Yuanzhen Xie, Tao Xie, Chengxiang Zhuo, Zang Li, Zibin Zheng:
L2CL: Embarrassingly Simple Layer-to-Layer Contrastive Learning for Graph Collaborative Filtering. CoRR abs/2407.14266 (2024) - 2023
- [j5]Jintang Li, Tao Xie, Liang Chen, Fenfang Xie, Xiangnan He, Zibin Zheng:
Adversarial Attack on Large Scale Graph. IEEE Trans. Knowl. Data Eng. 35(1): 82-95 (2023) - [j4]Jintang Li, Jiaying Peng, Liang Chen, Zibin Zheng, Tingting Liang, Qing Ling:
Spectral Adversarial Training for Robust Graph Neural Network. IEEE Trans. Knowl. Data Eng. 35(9): 9240-9253 (2023) - [c11]Jintang Li, Zhouxin Yu, Zulun Zhu, Liang Chen, Qi Yu, Zibin Zheng, Sheng Tian, Ruofan Wu, Changhua Meng:
Scaling Up Dynamic Graph Representation Learning via Spiking Neural Networks. AAAI 2023: 8588-8596 - [c10]Jintang Li, Jie Liao, Ruofan Wu, Liang Chen, Zibin Zheng, Jiawang Dan, Changhua Meng, Weiqiang Wang:
GUARD: Graph Universal Adversarial Defense. CIKM 2023: 1198-1207 - [c9]Jie Liao, Jintang Li, Liang Chen, Bingzhe Wu, Yatao Bian, Zibin Zheng:
SAILOR: Structural Augmentation Based Tail Node Representation Learning. CIKM 2023: 1389-1399 - [c8]Xinzhou Jin, Jintang Li, Yuanzhen Xie, Liang Chen, Beibei Kong, Lei Cheng, Bo Hu, Zang Li, Zibin Zheng:
Enhancing Graph Collaborative Filtering via Neighborhood Structure Embedding. ICDM 2023: 190-199 - [c7]Sheng Tian, Jihai Dong, Jintang Li, Wenlong Zhao, Xiaolong Xu, Baokun Wang, Bowen Song, Changhua Meng, Tianyi Zhang, Liang Chen:
SAD: Semi-Supervised Anomaly Detection on Dynamic Graphs. IJCAI 2023: 2306-2314 - [c6]Jintang Li, Ruofan Wu, Wangbin Sun, Liang Chen, Sheng Tian, Liang Zhu, Changhua Meng, Zibin Zheng, Weiqiang Wang:
What's Behind the Mask: Understanding Masked Graph Modeling for Graph Autoencoders. KDD 2023: 1268-1279 - [i22]Jintang Li, Sheng Tian, Ruofan Wu, Liang Zhu, Wenlong Zhao, Changhua Meng, Liang Chen, Zibin Zheng, Hongzhi Yin:
Less Can Be More: Unsupervised Graph Pruning for Large-scale Dynamic Graphs. CoRR abs/2305.10673 (2023) - [i21]Sheng Tian, Jihai Dong, Jintang Li, Wenlong Zhao, Xiaolong Xu, Baokun Wang, Bowen Song, Changhua Meng, Tianyi Zhang, Liang Chen:
SAD: Semi-Supervised Anomaly Detection on Dynamic Graphs. CoRR abs/2305.13573 (2023) - [i20]Jintang Li, Huizhe Zhang, Ruofan Wu, Zulun Zhu, Liang Chen, Zibin Zheng, Baokun Wang, Changhua Meng:
A Graph is Worth 1-bit Spikes: When Graph Contrastive Learning Meets Spiking Neural Networks. CoRR abs/2305.19306 (2023) - [i19]Jintang Li, Wangbin Sun, Ruofan Wu, Yuchang Zhu, Liang Chen, Zibin Zheng:
Scaling Up, Scaling Deep: Blockwise Graph Contrastive Learning. CoRR abs/2306.02117 (2023) - [i18]Jie Liao, Jintang Li, Liang Chen, Bingzhe Wu, Yatao Bian, Zibin Zheng:
SAILOR: Structural Augmentation Based Tail Node Representation Learning. CoRR abs/2308.06801 (2023) - [i17]Jintang Li, Zheng Wei, Jiawang Dan, Jing Zhou, Yuchang Zhu, Ruofan Wu, Baokun Wang, Zhang Zhen, Changhua Meng, Hong Jin, Zibin Zheng, Liang Chen:
Hetero$^2$Net: Heterophily-aware Representation Learning on Heterogenerous Graphs. CoRR abs/2310.11664 (2023) - [i16]Jintang Li, Jiawang Dan, Ruofan Wu, Jing Zhou, Sheng Tian, Yunfei Liu, Baokun Wang, Changhua Meng, Weiqiang Wang, Yuchang Zhu, Liang Chen, Zibin Zheng:
LasTGL: An Industrial Framework for Large-Scale Temporal Graph Learning. CoRR abs/2311.16605 (2023) - [i15]Yuchang Zhu, Jintang Li, Liang Chen, Zibin Zheng:
The Devil is in the Data: Learning Fair Graph Neural Networks via Partial Knowledge Distillation. CoRR abs/2311.17373 (2023) - [i14]Wangbin Sun, Jintang Li, Liang Chen, Bingzhe Wu, Yatao Bian, Zibin Zheng:
Rethinking and Simplifying Bootstrapped Graph Latents. CoRR abs/2312.02619 (2023) - 2022
- [j3]Liang Chen, Tao Xie, Jintang Li, Zibin Zheng:
Graph Enhanced Neural Interaction Model for recommendation. Knowl. Based Syst. 246: 108616 (2022) - [j2]Zhouxin Yu, Jintang Li, Liang Chen, Zibin Zheng:
Unifying multi-associations through hypergraph for bundle recommendation. Knowl. Based Syst. 255: 109755 (2022) - [c5]Zulun Zhu, Jiaying Peng, Jintang Li, Liang Chen, Qi Yu, Siqiang Luo:
Spiking Graph Convolutional Networks. IJCAI 2022: 2434-2440 - [c4]Bingzhe Wu, Yatao Bian, Hengtong Zhang, Jintang Li, Junchi Yu, Liang Chen, Chaochao Chen, Junzhou Huang:
Trustworthy Graph Learning: Reliability, Explainability, and Privacy Protection. KDD 2022: 4838-4839 - [i13]Liang Chen, Qibiao Peng, Jintang Li, Yang Liu, Jiawei Chen, Yong Li, Zibin Zheng:
Neighboring Backdoor Attacks on Graph Convolutional Network. CoRR abs/2201.06202 (2022) - [i12]Bingzhe Wu, Jintang Li, Chengbin Hou, Guoji Fu, Yatao Bian, Liang Chen, Junzhou Huang:
Recent Advances in Reliable Deep Graph Learning: Adversarial Attack, Inherent Noise, and Distribution Shift. CoRR abs/2202.07114 (2022) - [i11]Jintang Li, Jie Liao, Ruofan Wu, Liang Chen, Changhua Meng, Zibin Zheng, Weiqiang Wang:
GUARD: Graph Universal Adversarial Defense. CoRR abs/2204.09803 (2022) - [i10]Zulun Zhu, Jiaying Peng, Jintang Li, Liang Chen, Qi Yu, Siqiang Luo:
Spiking Graph Convolutional Networks. CoRR abs/2205.02767 (2022) - [i9]Bingzhe Wu, Jintang Li, Junchi Yu, Yatao Bian, Hengtong Zhang, Chaochao Chen, Chengbin Hou, Guoji Fu, Liang Chen, Tingyang Xu, Yu Rong, Xiaolin Zheng, Junzhou Huang, Ran He, Baoyuan Wu, Guangyu Sun, Peng Cui, Zibin Zheng, Zhe Liu, Peilin Zhao:
A Survey of Trustworthy Graph Learning: Reliability, Explainability, and Privacy Protection. CoRR abs/2205.10014 (2022) - [i8]Jintang Li, Ruofan Wu, Wangbin Sun, Liang Chen, Sheng Tian, Liang Zhu, Changhua Meng, Zibin Zheng, Weiqiang Wang:
MaskGAE: Masked Graph Modeling Meets Graph Autoencoders. CoRR abs/2205.10053 (2022) - [i7]Jintang Li, Zhouxin Yu, Zulun Zhu, Liang Chen, Qi Yu, Zibin Zheng, Sheng Tian, Ruofan Wu, Changhua Meng:
Scaling Up Dynamic Graph Representation Learning via Spiking Neural Networks. CoRR abs/2208.10364 (2022) - [i6]Zishan Gu, Jintang Li, Liang Chen:
Are All Edges Necessary? A Unified Framework for Graph Purification. CoRR abs/2211.05184 (2022) - [i5]Jintang Li, Jiaying Peng, Liang Chen, Zibin Zheng, Tingting Liang, Qing Ling:
Spectral Adversarial Training for Robust Graph Neural Network. CoRR abs/2211.10896 (2022) - 2021
- [j1]Liang Chen, Jiaying Peng, Yang Liu, Jintang Li, Fenfang Xie, Zibin Zheng:
Phishing Scams Detection in Ethereum Transaction Network. ACM Trans. Internet Techn. 21(1): 10:1-10:16 (2021) - [c3]Jintang Li, Kun Xu, Liang Chen, Zibin Zheng, Xiao Liu:
GraphGallery: A Platform for Fast Benchmarking and Easy Development of Graph Neural Networks Based Intelligent Software. ICSE (Companion Volume) 2021: 13-16 - [c2]Liang Chen, Jintang Li, Qibiao Peng, Yang Liu, Zibin Zheng, Carl Yang:
Understanding Structural Vulnerability in Graph Convolutional Networks. IJCAI 2021: 2249-2255 - [i4]Jintang Li, Kun Xu, Liang Chen, Zibin Zheng, Xiao Liu:
GraphGallery: A Platform for Fast Benchmarking and Easy Development of Graph Neural Networks Based Intelligent Software. CoRR abs/2102.07933 (2021) - [i3]Liang Chen, Jintang Li, Qibiao Peng, Yang Liu, Zibin Zheng, Carl Yang:
Understanding Structural Vulnerability in Graph Convolutional Networks. CoRR abs/2108.06280 (2021) - 2020
- [c1]Jintang Li, Zishan Gu, Qibiao Peng, Kun Xu, Liang Chen, Zibin Zheng:
Deep Insights into Graph Adversarial Learning: An Empirical Study Perspective. HBAI@IJCAI 2020: 87-101 - [i2]Liang Chen, Jintang Li, Jiaying Peng, Tao Xie, Zengxu Cao, Kun Xu, Xiangnan He, Zibin Zheng:
A Survey of Adversarial Learning on Graphs. CoRR abs/2003.05730 (2020) - [i1]Jintang Li, Tao Xie, Liang Chen, Fenfang Xie, Xiangnan He, Zibin Zheng:
Adversarial Attack on Large Scale Graph. CoRR abs/2009.03488 (2020)
Coauthor Index
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last updated on 2024-10-10 21:15 CEST by the dblp team
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